Is the negative of the policy loss function in a simple policy gradient algorithm an estimator of expected returns?
If I understand your question correctly, you are wondering whether the
policy gradient objective coincides with some real measure of progress.
This is exactly what the Policy Gradient Theorem proves (see Sutton et
al. (2000) or Sutton and Barto (2018), chapter 13). In particular,
policy gradient methods optimize the value of the start state $s_0$ under the
current policy, $v_\pi(s_0)$. Since this value is defined as an
expectation over returns, then your conclusion is correct.

Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning -
an Introduction. Adaptive Computation and Machine Learning. MIT Press.
http://www.worldcat.org/oclc/37293240.

Sutton, Richard S, David A McAllester, Satinder P Singh, and Yishay
Mansour. 2000. “Policy Gradient Methods for Reinforcement Learning with
Function Approximation.” In Advances in Neural Information Processing
Systems, 1057–63.